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EP3673246B1 - Système de mesure de sources de lumière et leurs procédés d'utilisation - Google Patents

Système de mesure de sources de lumière et leurs procédés d'utilisation

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Publication number
EP3673246B1
EP3673246B1 EP18848555.1A EP18848555A EP3673246B1 EP 3673246 B1 EP3673246 B1 EP 3673246B1 EP 18848555 A EP18848555 A EP 18848555A EP 3673246 B1 EP3673246 B1 EP 3673246B1
Authority
EP
European Patent Office
Prior art keywords
light
output
light source
spectral
input
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
EP18848555.1A
Other languages
German (de)
English (en)
Other versions
EP3673246A4 (fr
EP3673246C0 (fr
EP3673246A1 (fr
Inventor
Derek LEBLANC
Chris FELIX
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
BlueLight Analytics Inc
Original Assignee
BlueLight Analytics Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by BlueLight Analytics Inc filed Critical BlueLight Analytics Inc
Publication of EP3673246A1 publication Critical patent/EP3673246A1/fr
Publication of EP3673246A4 publication Critical patent/EP3673246A4/fr
Application granted granted Critical
Publication of EP3673246B1 publication Critical patent/EP3673246B1/fr
Publication of EP3673246C0 publication Critical patent/EP3673246C0/fr
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61C—DENTISTRY; APPARATUS OR METHODS FOR ORAL OR DENTAL HYGIENE
    • A61C19/00—Dental auxiliary appliances
    • A61C19/003—Apparatus for curing resins by radiation
    • A61C19/004—Hand-held apparatus, e.g. guns
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J1/00—Photometry, e.g. photographic exposure meter
    • G01J1/02—Details
    • G01J1/04—Optical or mechanical part supplementary adjustable parts
    • G01J1/0407—Optical elements not provided otherwise, e.g. manifolds, windows, holograms, gratings
    • G01J1/0422—Optical elements not provided otherwise, e.g. manifolds, windows, holograms, gratings using light concentrators, collectors or condensers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J1/00—Photometry, e.g. photographic exposure meter
    • G01J1/02—Details
    • G01J1/04—Optical or mechanical part supplementary adjustable parts
    • G01J1/0407—Optical elements not provided otherwise, e.g. manifolds, windows, holograms, gratings
    • G01J1/0474—Diffusers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J1/00—Photometry, e.g. photographic exposure meter
    • G01J1/42—Photometry, e.g. photographic exposure meter using electric radiation detectors
    • G01J1/4209—Photoelectric exposure meters for determining the exposure time in recording or reproducing
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J1/00—Photometry, e.g. photographic exposure meter
    • G01J1/42—Photometry, e.g. photographic exposure meter using electric radiation detectors
    • G01J1/4257—Photometry, e.g. photographic exposure meter using electric radiation detectors applied to monitoring the characteristics of a beam, e.g. laser beam, headlamp beam
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61C—DENTISTRY; APPARATUS OR METHODS FOR ORAL OR DENTAL HYGIENE
    • A61C19/00—Dental auxiliary appliances
    • A61C19/003—Apparatus for curing resins by radiation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J1/00—Photometry, e.g. photographic exposure meter
    • G01J1/42—Photometry, e.g. photographic exposure meter using electric radiation detectors
    • G01J1/429—Photometry, e.g. photographic exposure meter using electric radiation detectors applied to measurement of ultraviolet light
    • G—PHYSICS
    • G02—OPTICS
    • G02B—OPTICAL ELEMENTS, SYSTEMS OR APPARATUS
    • G02B5/00—Optical elements other than lenses
    • G02B5/02—Diffusing elements; Afocal elements

Definitions

  • Inadequately cured resins may result in reduced physical properties of the restoration, reduced bond strengths, increased wear and breakdown at the margins of the restoration, decreased biocompatibility, and increased DNA damage from leachates, such as bisphenol A diglycidylether methacrylate (Bis-OMA), tetraethyleneglycol dimethacrylate (TEGDMA), 1,6-bis(methacryloxy-2-ethoxycarbonylamino)-2,4,4-trimethylhexane (UDM), and 2,2-bis(4-(2-Methacryloxyethoxy)phenylpropane (bis-EMA).
  • Bis-OMA bisphenol A diglycidylether methacrylate
  • TEGDMA tetraethyleneglycol dimethacrylate
  • UDM 1,6-bis(methacryloxy-2-ethoxycarbonylamino)-2,4,4-trimethylhexane
  • bis-EMA 2,2-bis(4-(2-Methacryloxyethoxy)phenylpropane
  • the input data for the neural network is a function of the optical characteristic, e.g., responsivity curve, of the light detector. In some embodiments, the input data for the neural network is normalized to values between 0-1.
  • the system has a mobile device that communicates with the computer to provide, e.g., display, the output characteristic, e.g., the output power, of the light source. In one embodiment, the mobile device is a handheld device.
  • the light detector is a photodiode, a photomultiplier tube, a CCD array, a CMOS sensor, or a photovoltaic device. In some embodiments, the computer communicates wirelessly to the light detector.
  • the present disclosure further relates to a computer programmed with a neural network whose input data is a function of a signal produced by a light detector to determine an output characteristic, e.g., the output power, of a light source.
  • the neural network has a plurality of input nodes. Each input node is configured to contain at least one data point; a plurality of hidden nodes grouped in a plurality of layers, wherein each of the plurality of hidden nodes receives as input all of the at least one data points from the plurality of input nodes; and an output node, wherein the plurality of hidden nodes and output node are trained with the spectral profiles of a plurality of light sources including the light source being measured.
  • the hidden nodes and output node are further trained with an optical characteristic, e.g., the responsivity curve, of the light detector.
  • the data on each of the plurality of hidden nodes is summed before being passed to the plurality of hidden nodes in the next layer.
  • the data passed between hidden nodes is statistically weighted using the spectral profiles of a plurality of light sources including the light source and the optical characteristic, e.g., responsivity curve, of the light detector.
  • the plurality of hidden nodes contains a transfer function to update the statistical weights of each of the plurality of hidden nodes.
  • the derivative of the transfer function is used to update the statistical weights of each of the plurality of hidden nodes.
  • the transfer function is a sigmoidal.
  • the transfer function is a rectified function or a combination of sigmoidal and rectified, e.g., in different layers.
  • the data from the plurality of hidden nodes in the last of the plurality of layers are passed to the output node.
  • the output node contains a sigmoid transfer function. In some embodiments, wherein the output node returns a value representative of an output characteristic, e.g., the output power, of the light source.
  • the present disclosure further relates to a system for the determination of an output characteristic, e.g., the output power, of a light source, the system having a light collector; a light detector configured to produce a signal from light collected by the light collector; and a computer programmed with a neural network to provide an output characteristic, e.g., the output power, of the light source from input data corresponding to the signal produced by the light detector.
  • the neural network is trained with the spectral profiles of a plurality of light sources including the light source.
  • the neural network is trained with a plurality of input values for an optical characteristic, e.g., the responsivity curve, of the light detector.
  • the computer communicates wirelessly to the light detector.
  • the computer communicates wirelessly with a mobile device.
  • the mobile device is a handheld device.
  • the invention provides a method of determining an output characteristic, e.g., the output power, of a light source by collecting light from a light source with a light collector and non-spectral light detector to produce a signal; sending the signal to a computer programmed with a neural network to determine an output characteristic, e.g., the output power, of the light source; and providing, e.g., displaying, an output characteristic, e.g., the output power, to the user.
  • the signal produced from the non-spectral light detector is a voltage.
  • the computer communicates wirelessly to the non-spectral light detector.
  • the computer communicates wirelessly with the mobile device.
  • the mobile device is a handheld device.
  • the present invention provides a system for determining an output characteristic, e.g., output power, of a light source, e.g., a LCU used in dental restorations or a light used in photodynamic therapy.
  • a light source e.g., a LCU used in dental restorations or a light used in photodynamic therapy.
  • the devices, systems, and methods may be generally employed with any light source, including incandescent, laser, LED, halogen, fluorescent, plasma arc, or solar.
  • Information from the invention can be used to calculate exactly how much light is needed for a given process or procedure, e.g., to cure a photosensitive resin material without overexposure.
  • An advantage of the system is that it allows the end user to determine an output characteristic, e.g., power, without obtaining spectral data.
  • the light detected may be a subset of the spectrum produced by the light source.
  • various filters may be employed on the light source, the light collector, or separately to control the spectrum detected.
  • the light detected is in the visible range, e.g., between 360 and 540 nm.
  • An advantage of the system is that the light collector may communicate remotely, e.g., wirelessly, with the computer, allowing the measurement of an output characteristic, e.g., the output power, to be performed in most locations. This feature is advantageous as the light source may not be portable or easily moved to the location of the computer.
  • each of the light collector, light detector, and computer may be a separate component, or two or more of the components may be physically connected.
  • the computer or a part of it, may be in a physically different location than the light collector and/or light detector.
  • the light detector may interface with or be a part of a mobile device, e.g., cellular telephone or other handheld device, that can communicate with the computer, e.g., wirelessly.
  • Functions of the computer may also be distributed over several processors or cores, which may or may not be physically linked.
  • An exemplary light collector to be used as part of the system of the invention contains a light diffusing element that includes top portion that includes a screen and an optional aperture, a bottom portion that includes an inner surface that is substantially hemispherical, and a side portion that includes an inner surface that is substantially cylindrical.
  • the side portion further includes an outlet port.
  • the light diffusing element may or may not be enclosed within an external shell.
  • the side, bottom, and top portions may be manufactured from any suitable material, e.g., polytetrafluoroethylene (e.g., Teflon ® or Spectralon ® from Labsphere Inc.), polyoxymethylene (e.g., Delrin ® ), barium sulfate (e.g.,6080 White Reflectance Coating from Labsphere Inc.) or other Lambertian coating (e.g., Spectraflect ® or Duraflect ® from Labsphere Inc.). These portions may also include other materials, e.g., plastic, ceramic, glass, or metal, on which Lambertian materials are layered or coated.
  • suitable material e.g., polytetrafluoroethylene (e.g., Teflon ® or Spectralon ® from Labsphere Inc.), polyoxymethylene (e.g., Delrin ® ), barium sulfate (e.g.,6080 White Reflectance Coating from Labsphere Inc.) or other Lambertian
  • the screen is located above the side and bottom portions of light diffusing element of the light collector.
  • the screen may be sized to cover at least the aperture of light diffusing element.
  • the length of the screen may be equal to or greater than the diameter of the substantially hemispherical bottom portion.
  • the device may include a filter, e.g., glass (such as alkali-aluminosilicate sheet toughened glass (Gorilla ® glass)), neutral density filter, blue band filter, or a filter that filters wavelengths of at least 500 nm.
  • the filter may be located in the top portion of light diffusing element above or below the screen. In certain embodiments, the filter acts as a physical barrier to protect the screen from damage.
  • an aperture When an aperture is present in the top portion, it may include one or more tiered recesses into which the screen and any filter rest.
  • the tiered recesses provide physical support for the perimeter of the screen and filter.
  • Alternative ways of attaching a screen and/or filter are known in the art.
  • the screen may be part of a component that screws or clamps to the side and bottom portions.
  • the screen may also be a sheet of material that is compressed against the side portion, e.g., by the external housing.
  • the exterior shape of optional external shell may be substantially cubical, cylindrical, pyramidal, or a rectangular solid.
  • the internal surface and cavity shape of external shell may vary according to the external shape of the light diffusing element, e.g., it may conform to the exterior shape. In certain embodiments, as shown in Fig.
  • the light conducting conduit may be any suitable light conducting medium, such as a fiber optic cable or a liquid light guide. Other light conducting conduits are known in the art.
  • the light detector may be incorporated into the light collection device itself.
  • a light detector for use in the system of the invention may be any device capable of measuring the intensity of light and encoding the information in an electronic signal, e.g., a photodiode, a photomultiplier tube, a CCD array, a CMOS sensor, thermopile, or a photovoltaic device.
  • the detector is non-spectral, i.e., the detector measures the integrated intensity at all wavelengths of light.
  • An exemplary low cost light detector for use in the invention is a photodiode, as it produces a single value for the current (and thus the voltage) resulting from a light source irradiating its active area.
  • Non-spectral light detectors may respond differently to light at different wavelengths, according to an optical characteristic of the source, e.g., a responsivity curve ( Figure 3 ).
  • the optical characteristic e.g., responsivity curve
  • the optical characteristic can be used to compensate for this non-uniformity of a light source in the system of the invention.
  • spectral light detectors may also be employed, e.g., with a light collector having an outlet port separated from the inner surface by a diffusive material.
  • the data produced by the light collector and light detector is sent to a computer for processing and provision of the processed data to the user, e.g., by displaying the output characteristic.
  • the computer may receive the data from the light detector by way of a physical connection, such as a USB cable or similar hardware connection.
  • the data from the light detector may be sent to the computer via a wireless connection, such as optical, RF, or other wireless connection, e.g., Bluetooth ® , may be employed.
  • the computer system is programmed to process the data and provide the output characteristic, e.g., power, of the light source to the user. Programming may be via software, hardware, or a combination thereof.
  • the data from the light detector may be processed by a single program. Additionally or alternatively, multiple computer programs may be used in processing the data, and multiple computers may be employed in the processing or provision of the data.
  • the computer program may be programmed to recognize a number of variables about the system.
  • the computer may be programmed with both the spectra of the plurality of light sources it will be used to measure and with an optical characteristic, e.g., the responsivity curve, of the light detector, e.g. a photodiode.
  • an optical characteristic e.g., the responsivity curve
  • the spectrum of a light source will be substantially constant independent of the output intensity of the light source due to the use of identical components, e.g., LEDs, in its manufacture.
  • a light source that has a severely degraded intensity output will still have a nearly identical spectrum as a brand new light source of the same make.
  • the responsivity curve of a non-spectral light detector relates to the amount of photocurrent produced at every wavelength of light that impinges the detector's active area; the light detector produces a single value for the current corresponding to the integrated response of the light detector at all wavelengths in its range.
  • the computer may be programmed with an optical characteristic, e.g., responsivity curve, of the specific light detector used in the system and also may be user-selectable.
  • the computer includes a neural network for processing the signal from the light detector.
  • Neural networks are patterned mathematically to acquire, process, and interpret incoming information in a similar way to the human brain, e.g., by taking input information and passing it along to at least one "neuron", further propagating information until terminating at an output.
  • the neural network is able to improve the way in which it interprets an input signal, i.e., it learns from previous input signals, thereby improving the accuracy of the end result.
  • the "neurons” are typically organized in layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first (input), to the last (output) layer, possibly after traversing the layers multiple times, with each layer performing a mathematical manipulation on the data.
  • the training set data may be a combination of the spectra of the plurality of light sources to be measured by the light detector as well as an optical characteristic of the light detector, e.g., the responsivity curve, e.g., power per unit current (W/A) as a function of wavelength.
  • This information may be used to provide probabilistic conditions, e.g. values from 0 to 1, for what the "ideal" power of a light source should be at every wavelength capable of being converted into photocurrent on the light detector.
  • a neural network of a system of the invention is arranged into three components: the input layer, the hidden layer or layers, and the output layer; this design is known as a back-propagation architecture and a structural diagram of this architecture is showing in Figure 4 .
  • Each of the layers is divided into sub-units called nodes.
  • each of the nodes corresponds to a single datum point derived from the output signal of the light detector, e.g., photodiode.
  • the single datum point in each input node is then duplicated and placed into every node in the first of the hidden layers.
  • the signal from the light detector e.g., voltage from the photodiode
  • the wavelength values over the range of the wavelengths from the spectrum from a chosen light source are multiplied by the wavelength values over the range of the wavelengths from the spectrum from a chosen light source and normalized to produce a series of input values between 0 and 1.
  • These values are then summed together to produce a single value used as input for the transfer function of the neural network, which is a linear or non-linear function used to simulate the learning ability of biological neurons.
  • this function may be non-linear, e.g., a sigmoidal function, as it has an easily computable derivative.
  • the first derivative is used to calculate the error of the neutral network for improving the learning ability by updating the statistical weights.
  • the transfer function may be rectified.
  • the single value of the summed inputs is directed to the transfer function, e.g., sigmoidal or rectified, returning a single value.
  • the process of summing, passing through the transfer function, e.g., sigmoidal or rectified, and passing to the next layer's nodes is repeated for each of the hidden layers of the neural network.
  • Different transfer functions may be used in different layers. For example, the transfer function for one layer may be sigmoidal and may be rectified for another.
  • the final step in using a neural network of the invention is to pass the data from the final hidden node into the output layer, which includes a final round of summing the data from the nodes of the final hidden layer and passing it through the transfer function, e.g., sigmoidal or rectified, to produce a single output.
  • This output when un-normalized, returns the power of the light source.
  • the data is provided to the user.
  • the data can be provided by a wired device, such as a computer monitor, or can be a wireless device, e.g., a mobile device such as a cellular telephone or a tablet.
  • Data may be provided by any suitable means, e.g., visually in a display or audibly from a speaker. Such methods may provide numerical or other data, e.g., a color to signify a certain range of a numerical value.
  • the nature of the provision of data may depend on the output characteristic. For example, data on power, energy, irradiance, or cure time may be provided numerically.
  • Cure time may also be provided in the form of a countdown, which is either numerical or symbolic (e.g., an alarm or other indicator triggers after the cure time has elapsed). Data may also be provided directly to the light source, e.g., to control the length of exposure of the light source.
  • the output characteristic may be any measure that can be determined from the input data.
  • the output characteristics are output power, output energy, output flux, a calculated spectrum, irradiance, light source age, or calculated exposure time (e.g., time to cure a resin).
  • the output characteristic provided to the user may also be determined in steps.
  • the neural network may provide one output characteristic, e.g., power, which is used by the same or a different computer to determiner another characteristic, e.g., irradiance or cure time, according to known methods.
  • the invention features methods to determine an output characteristic, e.g., the output power, of a light source, e.g., using a computer programmed with a neural network.
  • the light from the light source is directed into a light collector such that the light is diffused by the light collector's inner surfaces and directed to a light detector. This diffused light impinges on the active area of a light detector, producing a signal representative of an output characteristic, e.g., the output power, of the light source.
  • the light detector is a non-spectral light detector, e.g., a photodiode.
  • This signal is then sent to the computer to provide an output characteristic, e.g., the output power, of the light source.
  • the computer Once the computer has processed the signal from the light detector, the resulting output characteristic, e.g., output power, of the light source is provided, e.g., displayed, to the user, e.g., on or via a mobile device in substantially real-time.
  • the computer communicates with the light detector wirelessly, e.g., RF, optical, or other communication standard. Further, the computer may be in wireless communication with the device providing, e.g., displaying, the data.
  • the device is a handheld device, e.g., a cellular telephone or a tablet.
  • the accuracy of the determination of an output characteristic, e.g., the output power, from a light source will depend on the number of individual measurements of an output characteristic, e.g., the output power, of the light source made during a measurement. This is determined by the length of time the active area of the light source is exposed to the light form the light source as well as the sampling frequency of the measurement, e.g., how many data points are collected per unit time.
  • Typical sampling times for measuring an output characteristic, e.g., the output power, of a light source are from about 1 second to about 1000 seconds, e.g., from about 1 second to about 100 seconds, from about 50 seconds to about 200 seconds, from about 150 seconds to about 300 seconds, from about 250 seconds to about 400 seconds, from about 350 seconds to about 500 seconds, from about 450 seconds to about 600 seconds, from about 550 seconds to about 700 seconds, from about 650 seconds to about 800 seconds, from about 750 seconds to about 900 seconds, or about 850 seconds to about 1000 seconds, e.g., about 1 second, about 2 seconds, about 3 seconds, about 4 seconds, about 5 seconds, about 6 seconds, about 7 seconds, about 8 seconds, about 9 seconds, about 10 seconds, about 50 seconds, about 100 seconds, about 150 seconds, about 200 seconds, about 250 seconds, about 300 seconds, about 350 seconds, about 400 seconds, about 450 seconds, about 500 seconds, about 550 seconds, about 600 seconds, about 650 seconds, about 700 seconds, about 750 seconds, about 800 seconds, about 850
  • the sampling frequency of the light detector varies between about 1 Hertz (Hz) to about 1000 Hz, e.g., from about 1 Hz to about 100 Hz, from about 50 Hz to about 200 Hz, from about 150 Hz to about 300 Hz, from about 250 Hz to about 400 Hz, from about 350 Hz to about 500 Hz, from about 450 Hz to about 600 Hz, from about 550 Hz to about 700 Hz, from about 650 Hz to about 800 Hz, from about 750 Hz to about 900 Hz, or about 850 Hz to about 1000 Hz, e.g., about 1 Hz, about 2 Hz, about 3 Hz, about 4 Hz, about 5 Hz, about 6 Hz, about 7 Hz, about 8 Hz, about 9 Hz, about 10 Hz, about 50 Hz, about 100 Hz, about 150 Hz, about 200 Hz, about 250 Hz, about 300 Hz, about 350 Hz, about 400 Hz, about 450 Hz,
  • the neural network When the neural network was fully trained, it takes 1024 input values matching the resolution of the wavelength values produced by the spectrometer. The resulting value is multiplied by the output intensity of the photodiode and subsequently normalized to a value between 0-1. For each input, a single value is produced. When these values are denormalized, the result is the power (in mW) of the light source.
  • a sampling frequency of 100 readings/second, i.e., 100 Hz, and a cure time of 10 seconds results in an input matrix of [1000,1024] data points that was input into the neural network, resulting in an output matrix of [1000,1].
  • the results of such output are shown in Figure 6A and 6B compared to the same type of measurement using a conventional spectrometer. Using the neural network, the resulting output power measurements come within 5% of the spectrometer.
  • the spectral profiles of four different curing lights were also input into the neural network as training data ( Figure 7 ).
  • the average output power of each of the four curing lights was measured using the photodiode-based neural network system and a conventional spectrometer. The results are shown in Figure 8 , and as before, the average power produced by the photodiode-based neural network system and the spectrometer are again within 5% of each other.
  • the system may be used to measure the power from any light source.
  • An exemplary application for a system of the invention with a light collector, light detector, a computer programmed with a neural network, and a mobile device is for measuring the output power of curing lights used in restoring dental work.
  • Figure 9 is a flow diagram of how a user, e.g., dentist or dental assistant, would use the system to measure the output power of a curing light.
  • the mobile device and the sensor are paired together over a wireless communications protocol, e.g., Bluetooth@ or other wireless transmission protocol.
  • the user can use the mobile device to control all aspects of the measurement.
  • the user can hit "Start" on the mobile device program and then expose the curing light to the light collector for an appropriated length of time.
  • the integrity of the data is verified, and then the raw data, e.g., normalized voltages from the light detector, are sent wirelessly to a computer programmed with a neural network configured to calculate the output power of the light source.
  • the data sent to the neural network includes the specific light source and light detector used to acquire the data, so the neural network uses the correct spectral profile and responsivity curve for its determination.
  • the resulting output power of the light source is displayed on the mobile device.

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Claims (15)

  1. Système destiné à déterminer une caractéristique de sortie d'une source de lumière, le système comprenant :
    a) un collecteur de lumière ;
    b) un détecteur de lumière non spectral conçu pour produire un signal à partir de la lumière collectée par le collecteur de lumière, le détecteur de lumière non spectral mesurant l'intensité intégrée à toutes les longueurs d'onde de lumière ; et
    c) un ordinateur programmé avec un réseau neuronal entraîné avec les profils spectraux d'une pluralité de sources de lumière ayant un spectre indépendant de l'intensité de sortie et incluant la source de lumière pour obtenir la caractéristique de sortie de la source de lumière à partir du signal produit par le détecteur de lumière non spectral,
    le réseau neuronal comprenant une pluralité de nœuds d'entrée, chaque nœud d'entrée étant conçu pour contenir au moins un point de données ;
    une pluralité de nœuds cachés regroupés dans une pluralité de couches, chaque nœud caché de la pluralité de nœuds cachés ayant reçu comme entrée la totalité de l'au moins un point de données depuis la pluralité de nœuds d'entrée ; et
    un nœud de sortie,
    la pluralité de nœuds cachés et le nœud de sortie étant entraînés avec les profils spectraux d'une pluralité de sources de lumière incluant la source de lumière,
    la caractéristique de sortie étant une puissance de sortie, une énergie de sortie, un flux de sortie, un spectre calculé, un éclairement énergétique, un âge calculé de la source de lumière, ou un temps d'exposition calculé.
  2. Système selon la revendication 1, dans lequel le réseau neuronal est également entraîné avec une pluralité de valeurs d'entrée pour une caractéristique optique du détecteur de lumière non spectral, la caractéristique optique étant la courbe de sensibilité du détecteur de lumière non spectral.
  3. Système selon l'une quelconque des revendications 1 à 2, comprenant en outre un dispositif mobile qui communique avec l'ordinateur pour obtenir la caractéristique de sortie de la source de lumière.
  4. Système selon l'une quelconque des revendications 1 à 3, dans lequel le détecteur de lumière non spectral est une photodiode, un tube photomultiplicateur, une matrice CCD, un capteur CMOS, ou un dispositif photovoltaïque.
  5. Système selon l'une quelconque des revendications 1 à 4, dans lequel l'ordinateur communique sans fil avec le détecteur de lumière non spectral.
  6. Ordinateur programmé avec un réseau neuronal dont les données d'entrée sont une fonction d'un signal produit par un détecteur de lumière non spectral pour déterminer une caractéristique de sortie d'une source de lumière lorsqu'on la fait fonctionner dans le cadre d'un système selon la revendication 1, le réseau neuronal comprenant :
    a) une pluralité de nœuds d'entrée, chaque nœud d'entrée étant conçu pour contenir au moins un point de données ;
    b) une pluralité de nœuds cachés regroupés dans une pluralité de couches, chaque nœud caché de la pluralité de nœuds cachés recevant comme entrée la totalité de l'au moins un point de données depuis la pluralité de nœuds d'entrée ; et
    c) un nœud de sortie,
    la pluralité de nœuds cachés et le nœud de sortie étant entraînés avec les profils spectraux d'une pluralité de sources de lumière ayant un spectre indépendant de l'intensité de sortie et incluant la source de lumière, la caractéristique de sortie étant une puissance de sortie, une énergie de sortie, un flux de sortie, un spectre calculé, un éclairement énergétique, un âge calculé de la source de lumière, ou un temps d'exposition calculé.
  7. Ordinateur selon la revendication 6, dans lequel les nœuds cachés et le nœud de sortie sont également entraînés avec une caractéristique optique du détecteur de lumière non spectral.
  8. Ordinateur selon la revendication 6 ou 7, dans lequel les données sur chaque nœud caché de la pluralité de nœuds cachés sont sommées avant d'être transférées à la pluralité de nœuds cachés dans la couche suivante.
  9. Ordinateur selon la revendication 7, dans lequel la caractéristique optique est la courbe de sensibilité du détecteur de lumière non spectral.
  10. Ordinateur selon l'une quelconque des revendications 6 à 9, dans lequel la pluralité de nœuds cachés comprend en outre une fonction de transfert pour calculer la caractéristique de sortie de la source de lumière.
  11. Ordinateur selon l'une quelconque des revendications 6 à 10, dans lequel les données issues de la pluralité de nœuds cachés dans la dernière couche de la pluralité de couches sont transférées au nœud de sortie et/ou dans lequel le nœud de sortie comprend en outre une fonction de transfert sigmoïde ou rectificateur.
  12. Procédé de détermination d'une caractéristique de sortie d'une source de lumière, comprenant :
    a) la collecte de la lumière provenant d'une source de lumière avec un collecteur de lumière et un détecteur de lumière non spectral pour produire un signal à partir de la lumière collectée par le collecteur de lumière, le détecteur de lumière non spectral mesurant l'intensité intégrée à toutes les longueurs d'onde de lumière ;
    b) l'envoi du signal à un ordinateur programmé avec un réseau neuronal entraîné avec les profils spectraux d'une pluralité de sources de lumière ayant un spectre indépendant de l'intensité de sortie et incluant la source de lumière pour déterminer la caractéristique de sortie de la source de lumière, le réseau neuronal comprenant une pluralité de nœuds d'entrée, chaque nœud d'entrée étant conçu pour contenir au moins un point de données ; une pluralité de nœuds cachés regroupés dans une pluralité de couches, chaque nœud caché de la pluralité de nœuds cachés ayant reçu comme entrée la totalité de l'au moins un point de données depuis la pluralité de nœuds d'entrée ; et un nœud de sortie, la pluralité de nœuds cachés et le nœud de sortie étant entraînés avec les profils spectraux d'une pluralité de sources de lumière incluant la source de lumière, la caractéristique de sortie étant une puissance de sortie, une énergie de sortie, un flux de sortie, un spectre calculé, un éclairement énergétique, un âge calculé de la source de lumière, ou un temps d'exposition calculé ; et
    c) la fourniture de la caractéristique de sortie à un utilisateur.
  13. Procédé selon la revendication 12, dans lequel l'ordinateur communique sans fil avec le détecteur de lumière non spectral.
  14. Procédé de détermination d'une caractéristique de sortie d'une source de lumière, comprenant :
    a) la réception d'un signal résultant de la lumière collectée depuis une source de lumière avec un collecteur de lumière et un détecteur de lumière non spectral qui mesure l'intensité intégrée à toutes les longueurs d'onde de lumière ; et
    b) l'utilisation du signal dans un ordinateur programmé avec un réseau neuronal entraîné avec les profils spectraux d'une pluralité de sources de lumière ayant un spectre indépendant de l'intensité de sortie et incluant la source de lumière pour déterminer la caractéristique de sortie de la source de lumière,
    le réseau neuronal comprenant une pluralité de nœuds d'entrée, chaque nœud d'entrée étant conçu pour contenir au moins un point de données ; une pluralité de nœuds cachés regroupés dans une pluralité de couches, chaque nœud caché de la pluralité de nœuds cachés ayant reçu comme entrée la totalité de l'au moins un point de données depuis la pluralité de nœuds d'entrée ; et un nœud de sortie, la pluralité de nœuds cachés et le nœud de sortie étant entraînés avec les profils spectraux d'une pluralité de sources de lumière incluant la source de lumière,
    la caractéristique de sortie étant une puissance de sortie, une énergie de sortie, un flux de sortie, un spectre calculé, un éclairement énergétique, un âge calculé de la source de lumière, ou un temps d'exposition calculé.
  15. Procédé selon la revendication 14, comprenant en outre la fourniture de la caractéristique de sortie à un utilisateur.
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US11850109B2 (en) 2023-12-26
US20200375711A1 (en) 2020-12-03
EP3673246A1 (fr) 2020-07-01

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